Are wild prey sufficient for the top predators in the lowland protected areas of Nepal?

A balanced equilibrium between carnivores and their prey is crucial for maintaining ecosystem sustainability. In this study, we applied the predator-prey power law equation to assess the balance between the biomass densities of carnivores and their wild prey within Nepal’s lowland protected areas during 2013, 2018, and 2022. The estimated value of the power-law exponent k for predator-prey biomass was 0.71 (95% CI = 0.39-1.05), indicating an approximate three-fold increase in predator biomass density for every five-fold increase in prey biomass density. Consequently, this creates a systematically bottom-heavy predator-prey biomass pyramid. This finding, consistent with the k=3/4 trophic biomass scaling across ecosystems, suggests that predator biomass is proportionally sustained by prey biomass, indicating a balance between top predators and their wild prey in Nepal's lowland protected areas. We further demonstrated it is possible to retain the overall power law exponent while jointly measuring intraguild competition between two predators with canonical correlation analysis. This understanding opens avenues for future research directed toward unraveling the factors that drive these consistent growth patterns in ecological communities.

Methods
The dataset for this study was collected from five lowland protected areas (PAs) in Nepal—Parsa, Chitwan, Banke, Bardia, and Shuklaphanta National Parks—focusing on large carnivores like tigers and leopards. Data on tiger densities (per 100 sq km) and prey densities (per sq km) were gathered from national tiger surveys and line-transect estimates conducted in 2013, 2018, and 2022 by the Department of National Parks and Wildlife Conservation (DNPWC), Nepal. Leopard densities (per 100 sq km) were obtained from various literature sources for the closest corresponding years.

The data was standardized per sq km for uniform comparisons across the parks. Biomass was calculated by multiplying species densities by their average weights, providing estimates for both prey and predator biomass per sq km across different years.

For analysis, a Bayesian approach using JAGS in R was employed to explore the predator-prey power law relationships for the combined biomass of predators and prey. The fundamental power law equation was log-transformed to fit within a linear regression framework. Linear models were created for total predator biomass density, tiger-only biomass density, and leopard-only biomass density. Additionally, canonical correlation analysis (CCA) was used to examine the relationships between tiger and leopard biomass and their shared prey biomass.